User Insight Engine

Overview

User research produces data. The Engine produces insight — the causal link between an observable behavior and the deep-layer driver that causes it. Three layers: Surface (what users say), Behavioral (what users do), Deep (why — cognitive and social drivers). Four Deep Layer drivers: Loss Aversion, Social Proof, Cognitive Load, Trust Cost.

Cross-skill: use after jobs-to-be-done to map interview output onto drivers; before feedback-loops to avoid optimizing for the wrong behavior; alongside confirmation-bias as a meta-check; alongside loss-aversion-prospect-theory when Loss Aversion is primary.

When to Use

Trigger: Surveys show satisfaction but behavioral data shows churn; a change that tested well failed in production; "why aren't users doing X?" where X is available and users expressed willingness; identical segments behaving differently; consistent drop-off that UX friction can't explain; low onboarding completion despite users rating it "easy"; feature adoption plateaued despite awareness.

When NOT to use: Zero behavioral data (don't attempt Deep Layer analysis); pre-launch no users (use lean-startup + jobs-to-be-done); B2B enterprise >12-month cycles (use principal-agent + signaling-games).

Coaching Novices (Adaptive Front Door)

• Engine mode: concrete behavior gap + behavioral data → run The Process directly.
• Coach mode: new to synthesis or no concrete case → guide step by step.